Digital compensation of Chromatic Dispersion (CD—the dependency of the phase velocity of an optical signal on its wavelength) and Polarization Mode Dispersion (PMD—modal dispersion where two different polarizations of light in a waveguide, propagate at different speeds, causing random spreading of the optical pulses) in 40 Gbp/s and 100 Gbp/s coherent optical fiber communication systems is of great interest nowadays. The common practice of CD and PMD compensation is to use fractional space equalizers, with two samples per symbol, or even more. In undistorted media, sampling at the symbol rate forms sufficient information to recover the digital data. However, when the channel introduces linear distortions such as CD and PMD, full reconstruction of the received analog signal is required in order to apply digital compensation. Sampling this signal at the symbol rate without preceding filtering violates the Nyquist sampling theorem, causing aliasing effect that results in performance degradation. On the other hand, using Anti Aliasing Filtering (AAF) prior to symbol rate sampling introduces substantial low-pass filtering which, in turn, causes substantial Inter Symbol Interference (ISI). The optimal equalizer, in the sense of minimum probability of error for a channel with ISI is the Maximum Likelihood Sequence Estimator (MLSE).
Several attempts of dealing with symbol space equalizers were made using AAF, in order to reduce cost and complexity of VLSI implementation. However, these attempts deal only with low CD values suffer from significant power penalty due to the combined effects of Aliasing and ISI.
In order to achieve better spectral efficiency, Dual Polarization (DP) modulation formats are typically used. Equalization can be generally divided into two categories:
Constant Equalization
A constant equalizer compensates for the bulk amount of CD and is widely described in prior art literature (for example, in references [1]-[3]). Since CD is polarization independent, there are typically two independent identical constant equalizers (one for each polarization).
Adaptive Equalization
An adaptive equalizer compensates for the following effects. The first effect is polarization mode dispersion (PMD), including polarization mixing and differential group delay (DGD) between the two polarization modes. The second effect is the portion of residual CD that was not compensated by the constant equalizers. The third effect is ISI introduced by bandwidth limited optoelectronics components at both the transmitting end (Tx) and the receiving end (Rx) portions of the link. Since the adaptive equalizer includes the polarization mixing cancellation, the digital signal processing is done jointly on both polarizations, which is also termed Multiple-In-Multiple-Out (MIMO) processing. Due to the complexity of joint processing implementation, most practical adaptive equalizers that are used for MIMO processing are Finite Impulse Response (FIR) filters with two complex input signals and two complex output signals.
“Coherent Compensation for 100G DP-QPSK with One Sample per Symbol Based on Anti-Aliasing Filtering and Blind Equalization MLSE” to Gorshtein et al (IEEE Photonics Technology Letters, vol. 22, No. 16, pp. 1208-1210, August 2010) suggests limiting the signal bandwidth by an anti aliasing filter (AAF) prior to symbol rate sampling, so as to obey Shannon sampling theorem. Thus the equalization can be achieved within this limited bandwidth, while the Inter Symbol Interference (ISI) introduced by the AAF is recovered, in turn, by means of Maximum Likelihood Sequence Estimation (MLSE) decoder. Since Gorshtein et al proposes a system with reduced bandwidth, there is additional ISI that is intentionally introduced by the AAF, the adaptive MIMO equalizer naturally compensates for this additional ISI, rather than allowing the MLSE to do so. However, this compensation suffers from severe noise enhancement, which is a well known drawback of equalization by (complex MIMO) FIR, especially in presence of both (residual) CD and PMD, thus introduces performance degradation.
All the methods described above have not yet provided satisfactory solutions to the problem of optimally equalizing the distortion of an optical data channel, with minimal performance degradation.
It is therefore an object of the present invention to provide a method for optimally equalizing the distortion of an optical data channel, while substantially reducing performance degradation, by modifying the adaptive equalization process.
Other objects and advantages of the invention will become apparent as the description proceeds.